Computing Testbeds as a Service

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https://portal.futuregrid.org

Computing Testbeds as a Service

or ? as an Instrument

July 21 2012

INCISE 2 Workshop Snowbird

Geoffrey Fox

[email protected]

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PolarGrid MRI

• CNS0723054 MRI: Acquisition of PolarGrid: Cyberinfrastructure for Polar Science

– Collaboration with Kansas CReSIS Science and Technology Center for Remote Sensing of Ice Sheets (world’s best radar to probe

ice/snow structure)

– Elizabeth City State University

• Multiple expeditions each year with nearing a petabyte of data total

• Field and Offline Analysis

• Outreach with ADMI (Association of Computer and Information Science/Engineering Departments at Minority Institutions) led by ECSU Linda Hayden

• High level Radar Analysis Algorithms/Software

• Led to several later successful projects/proposals exploiting PolarGrid Instrument

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https://portal.futuregrid.org ₄

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Offline Data Analysis on Polar Quarry

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Building High Level Tools

6 of XX

• Automatic Layer Determination developed by David Crandall

added to collaboration from the faculty at Indiana University

• Hidden Markov Method based Layer Finding Algorithm.

automatic layer finding algorithm manual method

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Some Lessons

• MRI only supported Instrument and its operation

• Need to couple with other projects to enable use

• Flourishing work on GPU’s for low power field

equipment to scalable Rad

• Good coupling of important science, computer

science, infrastructure and outreach to MSI’s

• Apply for REU supplements!

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https://portal.futuregrid.org ₈

PolarGrid laboratory at ECSU accessing FutureGrid

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FutureGrid Computing Testbed

as a Service

9

Private

Public FG Network

NID: Network Impairment Device

12TF Disk rich + GPU 512 cores

India (IBM) and Xray (Cray) (IU)

Alamo (TACC)

BravoSierra (SDSC)Delta (IU) Foxtrot (UF) Hotel (Chicago) 9

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https://portal.futuregrid.org ₁₀

• Computer Science

needs dynamic

instantiation at all

levels of stack

• Applications

develop

SaaS

services on top of

PaaS

middleware

IaaS

ØØ HypervisorBare Metal

Ø Operating System

Ø Virtual Clusters, Networks

PaaS

ØØ Cloud e.g. MapReduceHPC e.g. PETSc, SAGA

Ø Computer Science e.g. Haskell

Research Computing

aaS

Ø Custom Images

Ø Courses

Ø Consulting

Ø Portals

Ø Archival Storage

SaaS

Ø System e.g. SQL,GlobusOnline

Ø Applications e.g. Amber, Blast

Computing Testbed as a Service

Testbed-aaS Tools

Ø Provisioning

Ø Image Management

Ø IaaS Interoperability

Ø IaaS tools

Ø Expt management

Ø Dynamic Network

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What to use in Clouds: Cloud PaaS

• HDFS style

file system

to collocate data and computing

• Queues

to manage multiple tasks

• Tables

to track job information

• MapReduce

and

Iterative MapReduce

to support

parallelism

• Services

for everything

• Portals

as User Interface

• Appliances

and

Roles

as customized images

• Software tools like

Google App Engine, memcached

• Workflow

to link multiple services (functions)

• Data Parallel Languages

like Pig; more successful than

HPF?

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What to use in Grids and Supercomputers?

HPC (including Grid) PaaS

• Services Portals

and

Workflow

as in clouds

• MPI

and

GPU/multicore threaded

parallelism

• GridFTP

and high speed networking

• Wonderful libraries

supporting parallel linear algebra,

particle evolution, partial differential equation solution

• Globus, Condor, SAGA, Unicore, Genesis

for Grids

• Parallel I/O

for high performance in an application

• Wide area File System

(e.g. Lustre) supporting file sharing

• Scientific

Visualization

• Let’s unify Cloud and HPC PaaS and add Computer Science

PaaS

?

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Computer Science PaaS

• Tools to support Compiler Development

• Performance tools at several levels

• Components of Software Stacks

• Experimental language Support

• Messaging Middleware (Pub-Sub)

• Semantic Web and Database tools

• Simulators

• System Development Environments

• Open Source Software from Linux to Apache

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Research Computing as a Service

• Traditional Computer Center has a variety of capabilities supporting (scientific computing/scholarly research) users.

– Could also call this Computational Science as a Service

• IaaS, PaaS and SaaS are lower level parts of these capabilities but commercial clouds do not include

1) Developing roles/appliances for particular users

2) Supplying custom SaaS aimed at user communities

3) Community Portals

4) Integration across disparate resources for data and compute (i.e. grids)

5) Data transfer and network link services

6) Archival storage, preservation, visualization

7) Consulting on use of particular appliances and SaaS i.e. on particular software components

8) Debugging and other problem solving

9) Administrative issues such as (local) accounting

• This allows us to develop a new model of a computer center where commercial companies operate base hardware/software

• A combination of XSEDE, Internet2 and computer center supply 1) to 9)?

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aaS versus Roles/Appliances

• If you package a capability X as

XaaS

, it runs on a separate

VM and you interact with messages

– SQLaaS offers databases via messages similar to old JDBC model

• If you build a

role

or

appliance

with X, then X built into VM

and you just need to add your own code and run

– Venus-C Generic worker role builds in I/O and scheduling

• Lets take all capabilities – MPI, MapReduce, Workflow .. –

and offer as

roles

or

aaS

(or both)

• Perhaps workflow has a controller aaS with graphical

design tool while runtime packaged in a role?

• Need to think through packaging of parallelism

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27 Venus-C Azure Applications

chosen from ~70

16

Chemistry (3) • Lead Optimization in

Drug Discovery • Molecular Docking

Civil Eng. and Arch. (4) • Structural Analysis • Building information

Management

• Energy Efficiency in Buildings • Soil structure simulation

Earth Sciences (1)

• Seismic propagation

ICT (2)

• Logistics and vehicle routing

• Social networks analysis

Mathematics (1)

• Computational Algebra

Medicine (3)

• Intensive Care Units decision support.

• IM Radiotherapy planning. • Brain Imaging

Mol, Cell. & Gen. Bio. (7)

• Genomic sequence analysis • RNA prediction and analysis • System Biology

• Loci Mapping • Micro-arrays quality.

Physics (1)

• Simulation of Galaxies configuration

Biodiversity & Biology (2)

• Biodiversity maps in marine species • Gait simulation

Civil Protection (1)

• Fire Risk estimation and fire propagation

Mech, Naval & Aero. Eng. (2)

• Vessels monitoring

• Bevel gear manufacturing simulation

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FutureGrid and TestbedaaS

• FutureGrid is an international testbed modeled on Grid5000

– July 15 2012: 223 Projects, ~968 users

• Supporting international Computer Science and Computational Science research in cloud, grid and parallel computing (HPC)

• The FutureGrid uses TestbedaaS to make a flexible environment supporting interoperability, functionality, performance and

evaluation

• Static and Dynamic Provisioning

• See G. Fox, G. von Laszewski, J. Diaz, K. Keahey, J. Fortes, R. Figueiredo, S. Smallen, W. Smith, A. Grimshaw, FutureGrid - a reconfigurable testbed for Cloud, HPC and Grid Computing, Bookchapter – draft

Image1 Image2 … ImageN

Load

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5 Use Types for FutureGrid

TestbedaaS

•

223 approved projects (968 users) July 14 2012

– USA, China, India, Pakistan, lots of European countries

– Industry, Government, Academia

• Training Education and Outreach (

10%

)

– Semester and short events; interesting outreach to HBCU

• Computer science (

57%

)

– Core CS + Cyberinfrastructure

• Computer Systems Evaluation (

29%

)

– XSEDE (TIS, TAS), OSG, EGI

• Interoperability test-beds (

2%

)

– Grids and Clouds; Open Grid Forum OGF Standards

• New Domain Science applications (

26%)

– Life science highlighted (14%), Non Life Science (12%)

– Generalize to building Research Computing-aaS

18

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FutureGrid TestbedaaS Supports

Education and Training

• Jerome Mitchell HBCU Cloud View of Computing

workshop June 2011

• Cloud Summer School July 30—August 3 2012 with

10 HBCU attendees

• Mitchell and Younge building “Cloud Computing

Handbook” loosely based on my book with Hwang

and Dongarra

• Several classes around the world each semester

• Possible Interaction with (200 team) Student

Competition in China organized by Beihang Univ.

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FutureGrid TestbedaaS Supports Computer Science

• Core Computer Science

FG-172 Cloud-TM from Portugal: on

distributed concurrency control (software transactional

memory): "When Scalability Meets Consistency: Genuine

Multiversion Update Serializable Partial Data Replication,“ 32nd

International Conference on Distributed Computing Systems

(ICDCS'12) (top conference) used 40 nodes of FutureGrid

• Core Cyberinfrastructure

FG-42,45 LSU/Rutgers: SAGA Pilot Job

P* abstraction and applications. SAGA/BigJob use on clouds

• Core Cyberinfrastructure

FG-130: Optimizing Scientific

Workflows on Clouds. Scheduling Pegasus on distributed

systems with overhead measured and reduced. Used

Eucalyptus on FutureGrid

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Selected List of Services Offered

Cloud PaaS Hadoop Twister HDFS Swift Object Store IaaS Nimbus Eucalyptus OpenStack ViNE GridaaS Genesis II Unicore SAGA Globus HPCaaS MPI OpenMP CUDA TestbedaaS FG RAIN Portal Inca Ganglia Devops Exper. Manag./Pegasus

03/02/2020 [email protected] |

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Building Infrastructure

• Computing Testbed aaS

enables infrastructure to

dynamically support

Computer Science

(systems

experimentation)

aaS

• Natural is to build large systems and support large

experiments by federating hardware from several

sources

–

Requirement is that partners in federation agree on and

develop together

TestbedaaS

• Infrastructure includes networks, devices, field

equipment as in PolarGrid

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Image Metadata

Field Name Description

imgId Image’s unique identifier

owner owner

os Operating system

description Description of the image

tag Image’s keywords

vmType Virtual machine type

imgType Aim of the image

permission Access permission

imgStatus Status of the image createdDate Upload date

lastAccess Last time the image was accessed accessCount # times the image has been

accessed

size Size of the image

User Metadata

Field

Name Description

userId User’s unique identifier

fsCap Disk max usage (quota) fsUsed Disk space used

lastLogin Last time user used the framework

status Active, pending, disable

role Admin, User

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Register Images on Cloud

Number of Concurrent Requests

1 2 4 8

Ti me (s) 0 100 200 300 400 500 600 700 800 900

(1) Customize Image

Number of Concurrent Requests

1 2 4 8

Time (s) 0 100 200 300 400 500 600 700 800 900

(1) Customize Image

Eucalyptus

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Register Image on HPC

Number of Concurrent Requests1

Ti

me

(s)

0 20 40 60 80 100 120 140

(1) Retrieve Image from Repository

(2) Uncompress Image (3) Retrieve Kernels and Update xCAT Tables (4) Packimage (xCAT)

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Templated(Abstract) Dynamic Provisioning

27

• Abstract Specification of image mapped to various

HPC and Cloud environments

Essex replaces Cactus Current Eucalyptus 3 commercial while